Mixed-data sampling (MIDAS) regressions allow to estimate dynamic equations that explain a low-frequency variable by high-frequency variables and their lags. To account for temporal instabilities in this relationship, this paper discusses an extension to MIDAS with time-varying parameters, which follow random-walk processes. The non-linear functional forms in the MIDAS regression necessitate the use of non-linear ltering techniques. In this paper, the Particle Fi lter is used to estimate the time-varying parameters in the model. Simulations with time-varying DGPs help to assess the properties of the estimation approach. A real-time application to the relationship between daily corporate bond spreads and quarterly GDP growth in the Euro area shows that the leading indicator property of the spreads ahead of GDP has diminished during the recent crisis. During that period, corporate bond spreads rather seem to be coincident indicators of GDP growth.